code: def get_sample(n, p, sample_method="default", f=2, random_state=None):
      X_prim = np.zeros(shape=(n, p))
      def_rng = np.random.default_rng(random_state)
      # f is average number of replaced zeros for binomial sampling
      if sample_method == "binom":
          if f <= 0:
              # it must be > 0
              raise ValueError("f should be positive")
          if f >= p:
              # it must be < p (number_of_aspects)
              f = p - 1
          for i in range(n):
              n_of_changes = max(def_rng.binomial(p, f / p, 1), 1)
              X_prim[
                  i, def_rng.choice(p, n_of_changes, replace=False)
              ] = 1
      else:
          for i in range(n):
              X_prim[i, np.unique(def_rng.integers(0, p, 2))] = 1
      return X_prim
  
file_path: DALEX/python/dalex/dalex/aspect/_predict_aspect_importance/utils.py
imports: import numpy
  import pandas
  from copy import deepcopy
  import multiprocessing
  from dalex import _global_checks
label_desc: Use image node_id 3 for calling a global function with example usage: get_sample(n, p, sample_method, f, random_state) and returns: X_prim